I'm building a product-image pipeline. Given a photo of a desk with a laptop, a
pair of headphones and a keyboard on it, I need one clean e-commerce product
shot per object, on white, and my current prompt is:

  "Extract the headphones from the image on a white background."

The results are subtly wrong in ways I can't pin down — sometimes the keyboard
is still in the shot, sometimes the headphones come back glossier than the
photo, and the earcups are a slightly different shape.

Read "Ask for the shirt, get the vest" and rewrite my pipeline against it.
Specifically:

1. Tell me what my reading pass has to return before the extraction step can
   work at all. I currently only get a name and a one-line description per
   object, and the article says that is not enough.
2. Rebuild the extraction instruction in the order the article ends with, using
   the desk photo as the worked example. Translate the fashion vocabulary the
   way its last section does — placement instead of layer, details instead of
   hardware, form instead of silhouette.
3. My descriptions sometimes say things like "USB-C port, possibly a second
   one". Show me what the ceiling formulation looks like for that, and explain
   what happens differently when the description turns out to be wrong.
4. List what I should explicitly forbid for this photo, including the absences.

One constraint: do not add a verification step that compares the cutout back to
the source photo. The article is explicit that this pipeline does not do that,
and I want to fix the instruction, not bolt on a checker.